Introduction to wavelet transform

an ppt about introduction to wavelet transfrom

1. WELCOME Presented by, S.Rajendiran , 2013504016, MIT-Anna University, Chennai-44. Introduction to Wavelet Transform.

2. OUTLINE Overview Limitations of Fourier Transform Historical Development Principle of Wavelet Transform Examples of Applications Conclusion References

3. STATIONARITY OF SIGNAL Time Magnitude Magnitude Frequency (Hz) 2 Hz + 10 Hz + 20Hz Stationary Time Magnitude Magnitude Frequency (Hz) Non-Stationary 0.0-0.4: 2 Hz + 0.4-0.7: 10 Hz + 0.7-1.0: 20Hz

4. Limitations of Fourier Transform: To show the limitations of Fourier Transform, we chose a well-known signal in SONAR and RADAR applications, called the Chirp. A Chirp is a signal in which the frequency increases (‘up-chirp’) or decreases (‘down-chirp’).

5. Fourier Transform of Chirp Signals:

6. Different in time but same frequency representation !!! Fourier Transform only gives what frequency components exist in a signal. Fourier Transform cannot tell at what time the frequency components occur. However, Time-Frequency representation is needed in most cases. Result:

7. SOLUTION ?

8. SOLUTION 1

9. Short Time Fourier Analysis In order to analyze small section of a signal, Denis Gabor (1946), developed a technique, based on the FT and using windowing : STFT

10. STFT At Work

11. STFT At Work

12. What’s wrong with Gabor? Many signals require a more flexible approach - so we can vary the window size to determine more accurately either time or frequency.

13. SOLUTION 2

14. WAVELET TRANSFORM

15. Overview of wavelet: What does Wavelet mean? Oxford Dictionary: A wavelet is a small wave. Wikipedia : A wavelet is a mathematical function used to divide a given function or continuous-time signal into different scale components. A Wavelet Transform is the representation of a function by wavelets.

16. Historical Development: 1909 : Alfred Haar – Dissertation “On the Orthogonal Function Systems” for his Doctoral Degree. The first wavelet related theory . 1910 : Alfred Haar : Development of a set of rectangular basis functions. 1930s : - Paul Levy investigated “The Brownian Motion”. - Littlewood and Paley worked on localizing the contributing energies of a function. 1946 : Dennis Gabor : Used Short Time Fourier Transform . 1975 : George Zweig : The first Continuous Wavelet Transform CWT.

17. 1985 : Yves Meyer : Construction of orthogonal wavelet basis functions with very good time and frequency localization. 1986 : Stephane Mallat : Developing the Idea of Multiresolution Analysis “MRA” for Discrete Wavelet Transform “DWT”. 1988 : The Modern Wavelet Theory with Daubechies and Mallat . 1992 : Albert Cohen , Jean fauveaux and Daubechies constructed the compactly supported biorthogonal wavelets.

18. Here are some of the most popular mother wavelets :

19. Steps to compute CWT of a given signal : 1. Each Mother Wavelet has its own equation 2. Take a wavelet and compare it to section at the start of the original signal, and calculate a correlation coefficient C.

20. 2 . Shift the wavelet to the right and repeat step 1 until the whole signal is covered .

21. 3. Scale (stretch) the wavelet and repeat steps 1 through 2. 4 . Repeat steps 1 through 3 for all scales .

22. Haar transform

23. Time-frequency representation of « up-chirp » signal using CWT :

24. Applications:

25. FBI Fingerprints Compression: Since 1924, the FBI Collected about 200 Million cards of fingerprints. Each fingerprints card turns into about 10 MB, which makes 2,000 TB for the whole collection. Thus, automatic fingerprints identification takes a huge amount of time to identify individuals during criminal investigations.

26. The FBI decided to adopt a wavelet-based image coding algorithm as a national standard for digitized fingerprint records. The WSQ (Wavelet/Scalar Quantization) developed and maintained by the FBI, Los Alamos National Lab, and the National Institute for Standards and Technology involves:

27. JPEG 2000 Image compression standard and coding system. Created by Joint Photographic Experts Group committee in 2000. Wavelet based compression method. 1:200 compression ratio Mother Wavelet used in JPEG2000 compression

28. Comparison between JPEG and JPEG2000

29. MRA time-frequency Representation of Chirp Signal

30. 31 EE lab.530 WT compression

31. SUBBABD CODING ALGORITHM 0-1000 Hz D 2 : 250-500 Hz D 3 : 125-250 Hz Filter 1 Filter 2 Filter 3 D 1 : 500-1000 Hz A 3 : 0-125 Hz A 1 A 2 X[n]512 256 128 64 64 128 256 S S A 1 A 2 D 2 A 3 D 3 D 1

32. · 2-D Discrete Wavelet Transform · A 2-D DWT can be done as follows: Step 1: Replace each row with its 1-D DWT; Step 2: Replace each column with its 1-D DWT; Step 3: repeat steps (1) and (2) on the lowest subband for the next scale Step 4: repeat steps (3) until as many scales as desired have been completed original L H LH HH HL LL LH HH HL One scale two scales

33. 1 level Haar 1 level linear spline 2 level Haar Original Why is wavelet-based compression effective?

34. Image at different scales

35.   Entropy Original image 7.22 1-level Haar wavelet 5.96 1-level linear spline wavelet 5.53 2-level Haar wavelet 5.02 2-level linear spline wavelet 4.57 Why is wavelet-based compression effective? Coefficient entropies

36. Introduction to image compression For human eyes, the image will still seems to be the same even when the Compression ratio is equal 10 Human eyes are less sensitive to those high frequency signals Our eyes will average fine details within the small area and record only the overall intensity of the area, which is regarded as a lowpass filter. EE lab.530 38

37. Application: Image Denoising Using Wavelets Noisy Image: Denoised Image:

38. Image Denoising Using Wavelets Calculate the DWT of the image. Threshold the wavelet coefficients. The threshold may be universal or subband adaptive. Compute the IDWT to get the denoised estimate. Soft thresholding is used in the different thresholding methods. Visually more pleasing images.

39. A dvantages of using Wavelets: Provide a way for analysing waveforms in both frequency and duration. Representation of functions that have discontinuities and sharp peaks. Accurately deconstructing and reconstructing finite, non-periodic and/or non-stationary signals. Allow signals to be stored more efficiently than by Fourier transform.

40. Wavelets can be applied for many different purposes : Audio compression. Speech recognition. Image and video compression Denoising Signals Motion Detection and tracking

41. Wavelet is a relatively new theory , it has enjoyed a tremendous attention and success over the last decade , and for a good reason . Almost all signals encountred in practice call for a time- frequency analysis , and wavelets provide a very simple and efficient way to perform such an analysis . Still , there’s a lot to discover in this new theory , due to the infinite variety of non- stationary signals encountred in real life. Conclusion :

42. Questions?

43. For any queries: [email protected]